Search Results (90)

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article

Driven by climate change and stringent ecological conservation policies, arid and semi-arid pastoral areas face acute grassland degradation and forage–livestock imbalances. In Sunan County (Gansu Province, China), herders have increasingly turned to off-site grazing—leasing crop fields in adjacent oases during autumn and winter—to alleviate local grassland pressure and adapt their livelihoods. However, the interplay between the evolving land use system (L) and this emergent borrowed pasture system (B) remains under-explored. This study introduces a coupled analytical framework linking L and B. We employ multi-temporal remote sensing imagery (2018–2023) and official statistical data to derive land use dynamic degree (LUDD) metrics and 14 indicators for the borrowed pasture system. Through entropy weighting and a coupling coordination degree model (CCDM), we quantify subsystem performance, interaction intensity, and coordination over time. The results show that 2017 was a turning point in grassland–bare land dynamics: grassland trends shifted from positive to negative, whereas bare land trends turned from negative to positive; strong coupling but low early coordination (C > 0.95; D < 0.54) were present due to institutional lags, infrastructural gaps, and rising rental costs; resilient grassroots networks bolstered coordination during COVID-19 (D ≈ 0.78 in 2023); and institutional voids limited scalability, highlighting the need for integrated subsidy, insurance, and management frameworks. In addition, among those interviewed, 75% (15/20) observed significant grassland degradation before adopting off-site grazing, and 40% (8/20) perceived improvements afterward, indicating its potential role in ecological regulation under climate stress. By fusing remote sensing quantification with local stakeholder insights, this study advances social–ecological coupling theory and offers actionable guidance for optimizing cross-regional forage allocation and adaptive governance in arid pastoral zones. Full article

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies. Full article

Diversified application scenarios and business models are effective ways to improve the utilization and economic benefits of energy storage systems. In response to the current problems of single application scenarios, high idle rates, and imperfect price formation mechanisms faced by energy storage on the power generation side, a robust two-stage optimization operation strategy for shared energy storage is proposed, taking into account leasing demand and multiple uncertainties, from the perspective of the sharing concept. A multi-scenario application framework for shared energy storage is established to provide leasing services for wind farm clusters, as well as auxiliary services for participating in the electric energy markets and frequency regulation markets, and the participation sequence is streamlined. Based on the operating and opportunity costs of shared energy storage, a pricing mechanism for leasing services is designed to explore the driving forces of wind farm clusters participating in leasing services from the perspective of cost assessment. Considering the uncertainty of wind power output and market electric prices, as well as the market operational characteristics, an optimized operation model for shared energy storage in the day-ahead and real-time stages is constructed. In the day-ahead stage, a Stackelberg game model is introduced to depict the energy sharing between wind farm clusters and shared energy storage, forming leasing prices, leasing capacities, and energy storage pre-scheduling plans at different time periods. In the real-time stage, the real-time prediction results of wind power output and electric prices are integrated with scheduling decisions, and an improved robust optimization model is used to dynamically regulate the pre-scheduling plan for leasing capacity and shared energy storage. Based on actual data from the electricity market in Guangdong Province, effectiveness verification is conducted, and the results showed that diversified application scenarios improve the utilization rate of shared energy storage in the power generation side by 52.87%, increasing economic benefits by CNY 188,700. The proposed optimized operation strategy has high engineering application value. Full article

The bullwhip effect is the phenomenon of distorted information that causes the amplification of variability of demand in supply chains. We examine the relationship between the bullwhip effect and cost behavior using a large sample of U.S. public firms from 1980 to 2019. Our empirical results show that the costs of firms with a higher intensity of bullwhip effect are significantly more responsive to changes in sales, suggesting that firms facing higher amplification of demand will adopt a less rigid short-term cost structure with lower fixed and higher variable costs. Furthermore, the bullwhip effect is associated with a higher elasticity of number of employees, operating leases, and rental expenses with respect to sales. The findings of mediation analyses suggest that firms are likely to lease capacity resources to increase the flexibility and manage the operating risk associated with the bullwhip effect. The results are robust to alternative model specifications. This study contributes to both the cost accounting and supply chain management literature, and documents large sample evidence on whether and how the bullwhip effect affects a firm’s choice of cost structure. Full article

In the current conditions of development of the country’s market economy, the methodological support for cadastral land valuation requires effective modernization and improvement of the existing mechanisms for determining cadastral value for a fair distribution of land tax among landowners. In this regard, the aim of the study was to develop a methodology for taking into account the qualitative state of soils in the cadastral valuation of agricultural lands in the conditions of an active land market, as well as to modernize the method for taking into account the quality of soils within the framework of the income approach in the conditions of a depressed land market. The study was conducted based on a set of scientific methods: the analytical method was used to conduct an analysis of the scientific review of the problem area and to substantiate the relevance of the study, a cycle of laboratory experiments was conducted using mechanical and chemical analyses, the construction of thematic maps was carried out using the dispersion method, the regression modeling method was used to determine the cadastral value of garden plots, and the land rent capitalization method was used to calculate the cadastral value of agricultural land. Research results were as follows: Methodological recommendations were provided for taking into account the quality of soils in the form of an integral indicator of physical and chemical properties in the model for calculating the specific indicator of cadastral value (SICV) of garden and vegetable lands in the conditions of an active land market. The method of accounting for the qualitative state of soil fertility in the form of a weighted quality score of an agricultural land plot was modernized when determining the specific gross income within the framework of the land rent capitalization method used to calculate the SICV. Based on field work and laboratory experiments, current indicators of soil fertility status were obtained, and soil quality scores for Saint Petersburg were calculated. The possibility of using an integral indicator (soil quality score) as a cost factor instead of a large number of fertility status indicators was proven. Also, models for calculating the SICV of garden and vegetable plots were built for the conditions of an active land market, according to which the cadastral value of land plots in Saint Petersburg was calculated for subsequent land taxation. For agricultural lands, using the example of a land plot of a high-commodity agricultural enterprise (Leningrad Region), the cadastral value was also calculated using the proposed income approach method. The scientific significance of the study lies in the improvement of the methodological foundations of cadastral valuation, as well as the technology of taking into account the quality of soils when calculating the cadastral value. The practical significance of the study lies in the applicability of the results of soil quality assessment and models for calculating the SICV for land taxation; individual market valuation for lending, purchase, and sale; lease of agricultural land; and allocation of land plots on account of a land share. In the area of developing a set of melioration measures on agricultural lands, including the development and implementation of agricultural technologies and technical means to improve soil fertility, the results of laboratory studies to determine the physical and chemical properties of soils can be used. The obtained soil quality scores for Saint Petersburg are also applicable to identifying unused and degraded lands for their transfer to other types of use. Full article

Energy storage technology is a critical component in supporting the construction of new power systems and promoting the low-carbon transformation of the energy system. Currently, new energy storage in China is in a pivotal transition phase from research and demonstration to the initial stage of commercialization. However, it still faces numerous challenges, including incomplete business models, inadequate institutional policies, and unclear cost and revenue recovery mechanisms, particularly on the generation and grid sides. Therefore, this paper focuses on grid-side new energy storage technologies, selecting typical operational scenarios to analyze and compare their business models. Based on the lifecycle assessment method and techno-economic theories, the costs and benefits of various new energy storage technologies are compared and analyzed. This study aims to provide rational suggestions and incentive policies to enhance the technological maturity and economic feasibility of grid-side energy storage, improve cost recovery mechanisms, and promote the sustainable development of power grids. The results indicate that grid-side energy storage business models are becoming increasingly diversified, with typical models including shared leasing, spot market arbitrage, capacity price compensation, unilateral dispatch, and bilateral trading. From the perspectives of economic efficiency and technological maturity, lithium-ion batteries exhibit significant advantages in enhancing renewable energy consumption due to their low initial investment, high returns, and fast response. Compressed air and vanadium redox flow batteries excel in long-duration storage and cycle life. While molten salt and hydrogen storage face higher financial risks, they show prominent potential in cross-seasonal storage and low-carbon transformation. The sensitivity analysis indicates that the peak–valley electricity price differential and the unit investment cost of installed capacity are the key variables influencing the economic viability of grid-side energy storage. The charge–discharge efficiency and storage lifespan affect long-term returns, while technological advancements and market optimization are expected to further enhance the economic performance of energy storage systems, promoting their commercial application in electricity markets. Full article

Construction machinery operations are intrinsically linked to critical societal challenges, including safety risks and carbon emissions. In response to the high incidence of fatal accidents during installation and dismantling phases, the Chinese government has officially promoted integrated installation–dismantling services to enhance construction safety since 2023. However, the economic viability of this policy for leasing companies remains largely underexplored. To address this gap, this paper develops a leasing-oriented closed-loop construction machinery supply chain model that incorporates integrated installation–dismantling services under an industrial internet platform. The study first compares and analyzes the product leasing demand, installation and dismantling demand, and supply chain profits under both centralized and decentralized decision-making scenarios. Based on these analyses, a profit–cost sharing joint contract is designed to coordinate the supply chain. Furthermore, the interrelationships among key parameters are examined through a sensitivity analysis and numerical simulation. The results reveal that enhancing leasing information services increases both the demand for construction machinery and the platform’s operating costs. These costs are positively correlated with the product’s selling price, leading to higher purchasing costs for lessees. Similarly, improving information services for installation and dismantling raises the platform’s operating costs and enhances service levels, which in turn increases installation and dismantling costs for lessees. The findings demonstrate that within a certain range of cost-sharing and leasing-sharing proportional coefficients, the joint contract enables the supply chain to achieve Pareto optimization. This approach simultaneously alleviates economic pressure on lessees, improves construction safety, and promotes the integration of installation and dismantling services. Full article

To explore the operation strategy of battery leasing and battery swapping services (the two services), this paper constructs a battery swapping supply chain consisting of the battery manufacturer and the vehicle company. Taking the battery manufacturer as a core enterprise, this paper examines four service operation strategies: two self-operated services, self-operated battery swapping services, self-operated battery leasing services and two outsourcing services. Through comparative analysis, the findings indicate that the optimal strategy for the battery manufacturers depends on the vehicle body price. Specifically, when the vehicle body price is low, self-operating both services maximizes profitability and effectively stimulates demand for battery-swapping vehicles. Conversely, when the price is high, a complete outsourcing strategy is preferable, as it is the most effective way to stimulate battery-swapping vehicle demand. Similarly, the optimal strategy for the vehicle company is influenced by the vehicle body price. The vehicle company should provide the two services only when the vehicle body price is low; otherwise, they should focus on producing battery-swapping vehicles. Moreover, to stimulate demand for battery-swapping services, the determination of the optimal strategy is contingent upon several key variables, including the vehicle body price, the battery-swapping service price sensitivity, and the battery-swapping operating cost-sharing ratio. Full article

The proliferation of mobile terminal applications and the increasing energy consumption of chips have raised concerns about insufficient power in mobile user terminals. In response to this issue, this paper proposes a joint optimization algorithm for UAV-assisted caching and charging based on non-orthogonal multiple access (NOMA) within the context of mobile edge caching scenarios. The proposed algorithm considers the revenue generated from UAVs providing caching and charging services to users, as well as the cost associated with leasing cache files and the UAV energy consumption. The optimization problem aimed at maximizing UAV utility is established under constraints related to power and cache capacity. To address this mixed-integer programming problem, we divided it into two parts. The first part uses the Stackelberg–Bertrand game to optimize file pricing and the UAV cache strategy. In the second part, the block coordinate descent (BCD) method is used to optimize the UAV transmission power distribution, positioning, and user pairing. The joint optimization problem is divided into three subproblems, which use the Lagrange multiplier method, a simulated annealing algorithm, and a particle swarm optimization algorithm. Simulation results demonstrate that the proposed algorithm effectively reduces user transmission delay while also improving overall revenue generated by UAVs. Full article

Efficient supply–demand management in Product-as-a-Service (PaaS) markets requires tools to evaluate pricing strategies while integrating sustainability goals like reuse, efficiency, and carbon footprint reduction. This paper introduces a declarative modeling framework aimed at balancing the three pillars of profitability, cost optimization, and sustainability in PaaS markets. The framework addresses risks such as equipment failure, usage variability, and economic fluctuations, helping providers optimize pricing and operating costs while enabling customers to manage expenses. A declarative model is developed to assess the PaaS market balance to determine optimal leasing offers and requests for quotations. A case study is used to validate the framework, involving devices with specific rental prices and failure rates, as well as customer expectations and budget constraints. Computational experiments demonstrate the model’s practical applicability in real-world scenarios and it can be used by PaaS providers to develop competitive leasing strategies, policymakers to assess market stability, and enterprises to optimize procurement decisions. The findings show that the framework can guide decision making, offering insights into the impact of new technologies, compatibility conditions for leasing offers, and strategies for balancing providers’ profits and customers’ costs. The proposed framework has broad applicability across industries such as manufacturing, healthcare, logistics, and IT infrastructure leasing, where efficient resource allocation and lifecycle management are crucial. Full article

This paper presents a comparative study on active suspension controllers for ride comfort. Two types of active suspension controllers are designed and compared in terms of ride comfort: static output feedback (SOF) and parameter adaptive ones, which have identical controller structure. A quarter-car model is selected as a vehicle model. To date, LQR has been used as an active suspension controller. LQR is hard to implement in real vehicles due to the full-state measurement requirement. To avoid the full-state measurement of LQR, SOF control is selected as a controller structure in this paper. Suspension stroke and its rate are selected as sensor outputs for SOF and parameter active controllers. Two types of SOF controllers are designed. The first is the LQ SOF controller, designed with the state-space model and LQ cost function. The second is SOF controllers, designed by simulation-based optimization (SBOM) for the quarter-car model with nonlinear spring and damper. A parameter adaptive controller is designed with the recursive lease square (RLS) algorithm and its equivalent extended Kalman filter (EKF). For comparison, LQR is designed and used as a baseline. From simulation results, it is shown that the static output feedback and parameter adaptive controllers are equivalent to each other in terms of controller structure and ride comfort and which conditions are needed for better control performance on those controllers. Full article

In Thailand, droughts severely impact agriculture, particularly in non-irrigated areas, which comprise 76.4% of the country’s farmland. This highlights the need for sustainable energy solutions to mitigate environmental impacts. Despite government efforts, including over 900 Solar Water Pumping (SWP) demonstration units, many farmers remain hesitant to adopt this technology. This study examines the factors influencing farmers’ willingness to invest in SWP in Thailand’s drought-prone north and northeast regions, the most affected areas. Data were collected from 210 families—127 in the north (NC) and 83 in the northeast (NEC)—through surveys, interviews, and observations. Results show that 75.6% of NC and 77.1% of NEC farmers are willing to invest. However, barriers include financial constraints, reliance on government aid, uncertainty about returns, and lack of information. The estimated willingness-to-pay per household is USD 1438 in NC and USD 1518 in NEC, both exceeding the cost of a basic SWP system. Education, land ownership, and debt influence investment decisions, while the cultivation area impacts the amount invested. To increase adoption and combat climate change, tailored financial support, such as loan programs and leasing options, are needed for farmers in non-irrigated regions. Full article

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article